Title: Utilising pattern recognition algorithm to determine the possible speech score for Alzheimer's disease prediction

Authors: Pranab Hazra; Sudipta Banerjee; Kaushik Sarkar; Ashis Kumar Dhara; Tushar Kanti Bera

Addresses: Department of Electronics and Communication Engineering, Narula Institute of Technology, No. 81, Nilgunj Road, Agarpara, Kolkata, India ' Department of Electronics and Communication Engineering, Narula Institute of Technology, No. 81, Nilgunj Road, Agarpara, Kolkata, India ' Department of Electronics and Communication Engineering, Narula Institute of Technology, No. 81, Nilgunj Road, Agarpara, Kolkata, India ' Department of Electrical Engineering, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur – 713209, West Bengal, India ' Department of Electrical Engineering, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur – 713209, West Bengal, India

Abstract: This study employs machine learning (ML) to support the early-stage diagnosis of Alzheimer's disease (AD) using speech score data as a non-invasive, cost-effective alternative to traditional methods, like imaging and biomarker testing. Conventional techniques are often inaccessible, invasive and lack a strong correlation with cognitive decline. Speech-based assessments capture fine cognitive and linguistic deficits, early indicators of AD. The study employs six cognitive parameters: MoCA v7.1, HVLT-R (immediate and delayed recall), BNT-SF, and WMS-R Logical Memory I and II. A classification model integrating random forest, SVM and KNN was evaluated using k-fold cross-validation. ANOVA-based feature selection enhanced model performance, with random forest and SVM achieving high AUC scores of 0.98 and 0.97, respectively, outperforming KNN. Among features, MoCA and HVLT-R scores were most critical in distinguishing mild cognitive impairment (MCI) from healthy individuals. The findings highlight the potential of speech scores as early biomarkers for AD detection and monitoring.

Keywords: speech score data; physiological indicator; non-invasive diagnosis; feature selection; K-fold cross-validation; potential feature; classification model; validation; AUC.

DOI: 10.1504/IJBET.2025.149322

International Journal of Biomedical Engineering and Technology, 2025 Vol.49 No.1, pp.59 - 94

Received: 31 Jan 2025
Accepted: 05 May 2025
Published online: 24 Oct 2025 *

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